Theoretical Study of the Toxicity of a Series of Amides Herbicides Using Quantitative Structure-activity Relationships

DIOMANDE Sékou^1, , DJABAN Akoua Déborah¹, KONATE Mory Latif¹ and KONE Soleymane²

¹Department of Agro-Industrial Sciences and Technologies (AIST), UFR Agriculture, Halieutic Resources and Agro-Industry (AHRAI), University of San Pedro, San Pedro, Ivory Coast

²Department of Sciences of Structure and Matter (SSMT), Laboratory of Constitution and Reaction of Matter (LCRM), University of Félix Houphouët-Boigny, Abidjan, Ivory Coast

Cite this paper:
DIOMANDE Sékou, DJABAN Akoua Déborah, KONATE Mory Latif and KONE Soleymane. Theoretical Study of the Toxicity of a Series of Amides Herbicides Using Quantitative Structure-activity Relationships. Journal of Materials Physics and Chemistry. 2026; 14(1):1-10. doi: 10.12691/jmpc-14-1-1

Abstract

This QSAR study was carried out on a series of twenty-five (25) amides herbicides and highlights the importance of four (4) key descriptors that contribute to the lethal dose . These are polarizability (Pol), lipophilicity (LogP), total energy (), and chemical potential (µ). First, the molecular descriptors were determined using the DFT method with the B3LYP/6-31+G(d,p) theory level. Next, the theoretical lipophilicity was calculated using the open-source software A/LogPS 2.1. These descriptors were combined with biological activity using multiple linear regression (MLR) to develop the model. Finally, the domain of applicability (DA) was defined to avoid any hazardous extrapolation, and it appears that all molecular structures can be used through modulation for the prediction of new analogs. These must contain key groups such as halogens (I, Br, Cl), delocalized π systems (benzene ring, conjugated double bonds), and sulfur- or phosphorus-containing groups in their respective structures in order to exhibit optimal activity.

Keywords:
Amide herbicide Lethal dose QSAR DFT

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References:

[1]	S.M. Kumar, D.S. Kumar, S. Kumargupta, S.P. Pandey, R.yadav, Persistent organochlorine pesticides and polychlorinated biphenyls in intensive agricultural soils from North India. Asian Journal of Pharmaceutical Research, 2011, 1, 62.
[2]	Powles, S. B., & Yu, Q. Evolution in action: Plants resistant to herbicides. Annual Review of Plant Biology, 61, 317–347, 2010.
[3]	Carvalho, F. P. Pesticides, environment, and food safety.Food and Energy Security, 6(2), 48–60, 2017.
[4]	Mostafalou, S., & Abdollahi, M. Pesticides: an update of human exposure and toxicity. Archives of Toxicology, 91(2), 549–599 , 2017.
[5]	Chtita S., Modélisation de molécules organiques hétérocycliques biologiquement actives par des méthodes QSAR/QSPR. Recherche de nouveaux médicaments. PhD Thesis, 2017.
[6]	Patrick GL, Chimie pharmaceutique. Paris: De Boeck, 2003.
[7]	DIDI Mabrouka, Prédiction de la toxicité d’une série d’amides herbicides, Mémoire de Magister, 2010.
[8]	S.Chaltterjee, A.Hadi, B. Price, Regression Analysis by Examples; Wiley VCH: New York, USA, 2000.
[9]	Phuong HTN, thèse de doctorat «Synthèse et étude des relations structure/activité quantitatives (QSAR/2D) d’analogues Benzo [c] phénanthridiniques», Université d’Angers, (France), 2007.
[10]	Gaussian 09, Revision A.02, M. J. Frisch, G. W. Trucks, H. B. Schlegel, G. E. Scuseria, M. A. Robb, J. R. Cheeseman, G. Scalmani, V. Barone, B. Mennucci, G. A. Petersson, H. Nakatsuji, M. Caricato, X. Li, H. P. Hratchian, A. F. Izmaylov, J. Bloino, G. Zheng, J. L. Sonnenberg, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, T. Vreven, J. A. Montgomery, Jr., J. E. Peralta, F. Ogliaro, M. Bearpark, J. J. Heyd, E. Brothers, K. N. Kudin, V. N. Staroverov, R. Kobayashi, J. Normand, K. Raghavachari, A. Rendell, J. C. Burant, S. S. Iyengar, J. Tomasi, M. Cossi, N. Rega, J. M. Millam, M. Klene, J. E. Knox, J. B. Cross, V. Bakken, C. Adamo, J. Jaramillo, R. Gomperts, R. E. Stratmann, O. Yazyev, A. J. Austin, R. Cammi, C. Pomelli, J. W. Ochterski, R. L. Martin, K. Morokuma, V. G. Zakrzewski, G. A. Voth, P. Salvador, J. J. Dannenberg, S. Dapprich, A. D. Daniels, O. Farkas, J. B. Foresman, J. V. Ortiz, J. Cioslowski, and D. J. Fox, Gaussian, Inc., Wallingford CT,
[11]	P.K. Chattaraj, A.Cedillo, and R.G Parr, J. Phys.Chem., 103:7645, 1991.
[12]	P.W.Ayers, and R.G.Parr, J.Am Chem., Soc., 122:2010, 2000.
[13]	F. De Proft, J.M.L.Martin, and P. Geerlings, Chem. Phys. Let., 250:393, 1996.
[14]	P.Geerlings, F. De Proft, J.M.L.Martin, In Theoretical and Computational Chemistry; Seminario, J., Ed.,; Elsevier; Amsterdam, , 4 (Recent Developments in Density Functional Theory): 773, 1996.
[15]	F.DeProft, J.M.L. Martin, and P. Geerlings, Chem. Phys.Let., 256: 400, 1996.
[16]	Microsoft ® Excel ® 2016 (16.0.11029) MSO (16.0.11029) 64 Bits (2016) Partie de Microsoft Office Professionnel Plus.
[17]	XLSTAT Version 2021.2.2 (64 bit) Copyright 1995-2025 (2021) XLSTAT and AddinsoftwareRegistrered Trademarks of Addinsoft. https: //www.xlstat.com.
[18]	Chtita S. Modélisation de molécules organiques hétérocycliques biologiquement actives par des méthodes QSAR/QSPR. Recherche de nouveaux médicaments [thèse]. [Meknès]: Moulay Ismail; 2017.
[19]	Numbury Surendra Babu, Didugu Jayaprakash. Global and Reactivity Descriptors Studies of Cyanuric Acid Tautomers in Different Solvents by using of Density Functional Theory (DFT). Int J Sci Res IJSR; 4(6): 615‑20, 2015.
[20]	A. Vessereau, Méthodes statistiques en biologie et en agronomie. Lavoisier (Tec and Doc). Paris: 538, 1988.
[21]	G.W. Snedecor, W.G. Cochran, Statistical Methods; Oxford and IBH: New Delhi,India; 1967: 381.
[22]	M.V.Diudea, QSAR/QSAR Studies for Molecular Descriptors; Nova Science: Huntingdon, New York, USA, 2000.
[23]	E.X.Esposito, A.J.Hopfinger, J.D.Madura, Methods in Molecular Biology, 275: 131-213, 2004.
[24]	L.Eriksson, J. Jaworska, A. Worth, M.T.D. Cronin, R.M.Mc Dowell, P.Gramatica, Methods for Reliability and Uncertainly Assessment and for Applicability Evaluations of Classification and Regression-Based QSARs, Environmental Health Perspectives, 111(10): 1361-1375, 2003.
[25]	A. Golbraikh, A. Tropsha, Beware of q 2! J. Mol. Graph. Model., 20, 269–276, 2002.
[26]	T. M. Martin, P. Harten, D. M. Young, E.N. Muratov, A. Golbraikh, H. Zhu and A.Tropsha, Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling? J. Chem. Inf. Model., 52, 2570−2578, 2012.
[27]	Roy, P. P. and Roy, K. On some Aspects of Variable Selection for Partial Least Squares Regression Models. QSAR & combinatorial Science, 2008, 27, 302-313.
[28]	Partha Pratim Roy, Somnath Paul, Indrani Mitra and Kunal Roy, On Two Novel Parameters for Validation of Predictive QSAR Models. Molecules, 14, 1660-1701, 2009.
[29]	Ghamali M, Chtita S, Bouachrine M, Lakhlifi T. Méthodologie générale d’une étude RQSA/RQSP. Rev Interdiscip.; 1(1): 1-7, 2016.
[30]	Vinca P. Approches structure-propriété pour la prédiction des propriétés physico-chimiques des substances chimiques [Chimie théorique et informaique]. [Paris VI]: Pierre et Marie Curie,. 208p. 2013.
[31]	Dı́az-Garcı́a, J.A.; González-Farı́as, G. A note on the cook’s distance. J. Stat. Plan. Inference, 120, 119-136, 2004.
[32]	Militino, A.F.; Palacios, M.B.; Ugarte, M.D. Outliers detection in multivariate spatial linear models. J. Stat. Plan. Inference, 136, 125-146, 2006.